US10719733B2 - Artificially rendering images using interpolation of tracked control points - Google Patents
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Definitions
- the present disclosure relates to artificially rendering images using viewpoint interpolation and/or extrapolation.
- Producing combined images such as a panorama, or a three-dimensional (3D) image or model requires combining data from multiple images and can require interpolation or extrapolation of data.
- Most previously existing methods of interpolation or extrapolation require a significant amount of data in addition to the available image data.
- the additional data needs to describe the scene structure in a dense way, such as provided by a dense depth map (where for every pixel a depth value is stored) or an optical flow map (which stores for every pixel the motion vector between the available images). This limits the efficiency of these methods in processing speed as well as transfer rates when sending it over a network. Accordingly, improved mechanisms for interpolation and extrapolation are desirable.
- a set of control points is tracked between a first frame and a second frame, where the first frame includes a first image captured from a first location and the second frame includes a second image captured from a second location.
- An artificially rendered image corresponding to a third location is then generated by interpolating individual control points for the third location using the set of control points and interpolating pixel locations using the individual control points.
- the individual control points are used to transform image data.
- a computer readable medium for artificially rendering images using interpolation of tracked control points includes computer code for tracking a set of control points between a first frame and a second frame, where the first frame includes a first image captured from a first location and the second frame includes a second image captured from a second location.
- the computer readable medium also includes computer code for generating an artificially rendered image corresponding to a third location. This image is generated by interpolating individual control points for the third location using the set of control points and interpolating pixel locations using the individual control points. The individual control points are used to transform image data.
- a method for artificially rendering images using interpolation of tracked control points includes tracking a set of control points between numerous frames and generating a panoramic representation from numerous frames, where the numerous frames are associated with a first layer and include a first frame corresponding to a first image captured from a first location and a second frame corresponding to a second image captured from a second location. The method also includes generating an artificially rendered layer from the panoramic representation by interpolating individual control points from the set of control points and interpolating pixel locations using the individual control points.
- FIG. 1 illustrates an example of a configuration where an artificially rendered image is generated for a chosen viewpoint on a trajectory between two frames.
- FIG. 2 illustrates an example of combining image information gathered from two frames to artificially render an image for another frame using weighted image information.
- FIG. 3 illustrates an example of a process for generating an artificially rendered image from two frames.
- FIG. 4 illustrates an example of combining image information gathered from two frames to artificially render an image in another frame using multiple layers extracted from surround views.
- FIGS. 5A-5B illustrate an example of an image transformation between two frames using tracked control points between the two frames.
- FIG. 6 illustrates an example of viewpoint extrapolation outside of the trajectory between two frames using multiple layers and the parallax effect.
- FIG. 7 illustrates an example of a process for extrapolating a viewpoint outside the trajectory between two frames.
- FIG. 8 illustrates an example of a process for interpolating a layer using a panoramic representation.
- FIG. 9 illustrates an example of a process for generating an artificially rendered image from a surround view.
- FIG. 10 illustrates a particular example of a computer system that can be used with various embodiments of the present invention.
- a surround view which eliminates redundancy in the data, and presents a user with an interactive and immersive active viewing experience
- U.S. patent application Ser. No. 14/530,669 by Holzer et al. filed on Oct. 31, 2014, titled “Analysis and Manipulation of Images and Video for Generation of Surround Views,” which is incorporated by reference herein in its entirety and for all purposes.
- a surround view provides a user with the ability to control the viewpoint of the visual information displayed on a screen.
- a surround view is a multi-view interactive digital media representation.
- the data used to generate a surround view can come from a variety of sources.
- data such as, but not limited to, two-dimensional (2D) images can be used to generate a surround view.
- 2D images can include color image data streams such as multiple image sequences, video data, etc., or multiple images in any of various formats for images, depending on the application.
- Another source of data that can be used to generate a surround view includes location information. This location information can be obtained from sources such as accelerometers, gyroscopes, magnetometers, GPS, WiFi, IMU-like systems (Inertial Measurement Unit systems), and the like.
- depth images can include depth, 3D, or disparity image data streams, and the like, and can be captured by devices such as, but not limited to, stereo cameras, time-of-flight cameras, three-dimensional cameras, and the like.
- the data can then be fused together.
- a surround view can be generated by a combination of data that includes both 2D images and location information, without any depth images provided.
- depth images and location information can be used together.
- image data can be used with location information, depending on the application and available data.
- the data that has been fused together is then used for content modeling and context modeling.
- the subject matter featured in the images can be separated into content and context.
- the content can be delineated as the object of interest and the context can be delineated as the scenery surrounding the object of interest.
- the content can be a three-dimensional model, depicting an object of interest, although the content can be a two-dimensional image in some embodiments.
- the context can be a two-dimensional model depicting the scenery surrounding the object of interest.
- the context can also include three-dimensional aspects in some embodiments.
- the context can be depicted as a “flat” image along a cylindrical “canvas,” such that the “flat” image appears on the surface of a cylinder.
- some examples may include three-dimensional context models, such as when some objects are identified in the surrounding scenery as three-dimensional objects.
- the models provided by content modeling and context modeling can be generated by combining the image and location information data.
- context and content of a surround view are determined based on a specified object of interest.
- an object of interest is automatically chosen based on processing of the image and location information data. For instance, if a dominant object is detected in a series of images, this object can be selected as the content.
- a user specified target can be chosen. It should be noted, however, that a surround view can be generated without a user specified target in some applications.
- one or more enhancement algorithms can be applied.
- various algorithms can be employed during capture of surround view data, regardless of the type of capture mode employed. These algorithms can be used to enhance the user experience. For instance, automatic frame selection, stabilization, view interpolation, filters, and/or compression can be used during capture of surround view data.
- these enhancement algorithms can be applied to image data after acquisition of the data. In other examples, these enhancement algorithms can be applied to image data during capture of surround view data.
- view interpolation can be used to improve the viewing experience.
- synthetic, intermediate views can be rendered on the fly. This can be informed by content-weighted keypoint tracks and IMU information, as well as by denser pixel-to-pixel matches. If depth information is available, fewer artifacts resulting from mismatched pixels may occur, thereby simplifying the process.
- view interpolation can be applied during capture of a surround view in some embodiments. In other embodiments, view interpolation can be applied during surround view generation.
- a surround view is generated after any enhancement algorithms are applied.
- the surround view can provide a multi-view interactive digital media representation.
- the surround view can include a three-dimensional model of the content and a two-dimensional model of the context.
- the context can represent a “flat” view of the scenery or background as projected along a surface, such as a cylindrical or other-shaped surface, such that the context is not purely two-dimensional.
- the context can include three-dimensional aspects.
- surround views provide numerous advantages over traditional two-dimensional images or videos. Some of these advantages include: the ability to cope with moving scenery, a moving acquisition device, or both; the ability to model parts of the scene in three-dimensions; the ability to remove unnecessary, redundant information and reduce the memory footprint of the output dataset; the ability to distinguish between content and context; the ability to use the distinction between content and context for improvements in the user-experience; the ability to use the distinction between content and context for improvements in memory footprint (an example would be high quality compression of content and low quality compression of context); the ability to associate special feature descriptors with surround views that allow the surround views to be indexed with a high degree of efficiency and accuracy; and the ability of the user to interact and change the viewpoint of the surround view.
- surround views can be used to enhance various fields such as e-commerce, visual search, 3D printing, file sharing, user interaction, and entertainment.
- view interpolation can be used to improve the viewing experience.
- synthetic, intermediate views can be rendered during acquisition or generation of a surround view.
- only a limited amount of image frames are stored for each surround view for storage efficiency.
- the selection of these frames is based on the motion of the camera rather than on time. For instance, a new frame is sampled only if the camera moved sufficiently with respect to the previously sampled frame. Consequently, in order to provide a smooth viewing experience for a user interacting with a surround view, artificially rendered frames must be generated. These artificially rendered frames serve to close the gap between two sampled frames that would otherwise appear to have a “jump” between them. Accordingly, by generating these artificially rendered frames, smooth navigation within the surround view becomes possible.
- interpolation between frames is performed along a path between the frames. In some instances, this path can follow a non-linear trajectory.
- various described extrapolation methods allow the artificially rendered frame to represent a view from a location that is not on the trajectory of the recording camera or in between two sampled frames. By providing interpolation and extrapolation methods for paths between or beyond the originally provided frames, various artificially rendered views can be provided for a surround view.
- FIG. 1 shown is an example of a configuration where an artificially rendered image is generated for a viewpoint at Location X on a trajectory between Location A and Location B.
- a single transform for viewpoint interpolation is used along the trajectory between two frames, Frame A 102 and Frame B 104 .
- Frame A 102 represents an image captured of objects 108 and 110 by a camera 112 located at Location A 122 .
- Frame B 104 represents an image captured of object 108 by a camera 114 located at Location B 124 .
- the transformation (T_AB) is estimated between the two frames, where T_AB maps a pixel from frame A to frame B. This transformation is performed using methods such as homography, affine, similarity, translation, rotation, or scale.
- an artificially rendered image at Location X 126 which can also be denoted as a viewpoint position at x ⁇ in [0, 1] on the trajectory between frame A and B, where frame A is located at 0 and frame B at 1, is then generated by interpolating the transformation, gathering image information from Frames A and B, and combining the image information.
- the transformation is interpolated (T_AX and T_XB).
- One way to interpolate this transformation is to parameterize the transformation T_AB and linearly interpolate those parameters.
- this interpolation is not limited to linear interpolations and other methods can be used within the scope of this disclosure.
- image information is gathered from both Frames A and B by transferring image information from Frame A 102 to Frame X 106 based on T_AX and by transferring image information from Frame B 104 to Frame X 106 based on T_XB. Finally, the image information gathered from both Frames A and B is combined to generate an artificially rendered image at Location X 126 .
- combining the image information gathered from both Frames A and B can be done in various ways.
- FIG. 2 shown is one example of a way to combine image information gathered from both Frames A and B to artificially render an image in Frame X using weighted image information.
- image information is taken from Frame A 202 and Frame B 204 to generate an image in Frame X 206 .
- Image information in Region 208 of Frame X 206 is taken from Frame A.
- Image information in Region 210 of Frame X 206 is taken from Frame B.
- Image information in Region 210 of Frame X 206 includes information from both Frame A 202 and Frame B 204 .
- the image information is weighted. Specifically, the information is combined using the image information from Frame A 202 using 1- x and the image information from Frame B 204 using x.
- a transformation is estimated between a first frame and a second frame at 302 .
- the transformation is estimated using methods such as homography, affine, similarity, translation, rotation, or scale.
- the first frame includes a first image captured from a first location (Location A 122 ) and the second frame includes a second image captured from a second location (Location B 124 ).
- the first image and second image are associated with the same layer.
- an artificially rendered image is rendered corresponding to a third location. This third location is positioned on a trajectory between the first location and the second location, as shown as Location X 126 in FIG. 1 .
- the artificially rendered image is generated in a number of steps.
- a transformation is interpolated from the first location to the third location and from the third location to the second location at 304 .
- interpolating the transformation includes parameterizing the transformation from the first location to the third location and from the third location to the second location.
- the parameterized transformation is then linearly interpolated.
- image information is gathered from the first frame and the second frame at 306 by transferring first image information from the first frame to the third frame based on the interpolated transformation and second image information from the second frame to the third frame based on the interpolated transformation.
- the first image information and the second image information is combined to yield the artificially rendered image.
- combining the first image information and the second image information includes weighting the first image information from the first frame and the second image information from the second frame.
- the process can end with combining the first image information and second image information at 308 .
- the process shown continues with determining whether an occlusion is present in the artificially rendered image at 310 .
- Such an occlusion is created by a change in layer placement between the first frame and second frame.
- the first image and second image may include foregrounds and backgrounds that include one or more layers, each layer corresponding to a region of image pixels that share a similar motion, as described in more detail below with regard to FIG. 4 . These layers can move relative to each other. In some cases, the layers may move in a manner causing an occlusion in a combined image.
- the missing data is replaced to fill the first occlusion at 312 .
- the missing data is obtained by interpolation or extrapolation.
- the missing data is obtained from the first frame or the second frame.
- the missing data is obtained from another frame (separate from the first and second frame) that includes image information corresponding to the missing data.
- the missing data is obtained by extrapolating from surrounding image data. This surrounding image data includes information from frames located near the first location and second location.
- the present example uses the method described above with regard to FIG. 3 , where the first frame and second frame are made up of two or more layers.
- the first frame which includes a view at Time A 302 , includes a background layer 306 and foreground layer 304 .
- the second frame which includes a view at Time B 308 , includes a background layer 312 and foreground layer 310 .
- foreground and/or background can include one or more layer.
- a layer is a region of image pixels that share a similar motion, based on factors such as depth and or speed of the corresponding real world object.
- viewpoint-interpolation can be used to generate an artificially rendered image as described above with regard to FIG. 3 .
- the process can also include methods for filling the regions corresponding to any detected occlusions. Whether data is occluded or missing is based on the ordering of layers. Although the layers are expected to stay in the same order, they can switch order if the objects change their depth location within the scene. For instance, occlusions are created if a first layer moves in front of a second layer, where the first layer is closer to the camera than second layer.
- image information from the first layer is taken wherever the first and second layer overlap.
- image information is taken from both layers according to the opacity of the first layer. This process can also be extended to more than two layers.
- missing information can be obtained in various ways, including interpolation or extrapolation.
- missing data can be taken from only one of the frames, depending on which frame includes a view of the missing data.
- the missing data can be taken from other frames where this region is visible. For instance, in a surround view that includes various frames and viewpoints, other frames can be used to fill in this missing data.
- the missing data can be extrapolated from surrounding image data, to create artificial image data that estimates what the missing data would look like.
- viewpoint-extrapolation using multiple layers can be applied outside of the trajectory between the two frames, by moving the different layers in perpendicular motion to the trajectory motion, as shown in more detail with regard to FIG. 6 .
- the transform between the two frames can be an image transformation or a 3D transformation.
- An image transformation can use methods such as homography, affine, similarity, translation, rotation, scale.
- layers are moved with respect to their depth and the 3D transformation, according to various embodiments.
- FIGS. 5A-5B shown is an example of an image transformation between two frames using tracked control points between the two frames.
- FIG. 5A shows a first frame of a scene at a first time T 1
- FIG. 5B shows a second frame of the scene at a second time T 2 .
- a set of control points 502 , 504 , 506 , 508 , 510 , and 512 are tracked between the two frames, such that their locations are known in both frames.
- An intermediate frame X at x ⁇ in [0, 1] between these two frames can be generated by interpolating the location of all control points individually for x and then computing the image data for frame X by interpolating all pixel locations using those control points.
- the set of control points are located within a single layer that includes particular objects viewable in the first frame and second frame.
- generating an artificially rendered image may include interpolating image data for multiple layers and combining these layers.
- each pixel in the first frame corresponds to a control point in the set of control points. Accordingly, each pixel is tracked and interpolated in that instance.
- an artificially rendered image is generated as intermediate frame X by tracking a set of control points between the first frame and the second frame, and interpolating individual control points for the third location X using the set of control points and interpolating pixel locations using the individual control points.
- the first frame includes a first image captured from a first location and the second frame includes a second image captured from a second location.
- interpolating the pixel locations includes interpolation using barycentric coordinates based on three or more control points.
- interpolating the pixel locations includes interpolation using splines.
- interpolating the pixel locations includes interpolation using finite elements.
- interpolating the pixel locations includes using motion of the nearest control point.
- interpolating pixel locations using the individual control points includes using dense optical flow.
- interpolating pixel locations using the individual control points includes using depth data.
- a scene 408 includes multiple layers Layer A 402 , Layer B 404 , and Layer C 406 .
- two frames capturing images of the scene 408 can be obtained by moving a camera along camera motion 416 . Comparing the two images will show parallax motion in the direction 412 .
- the amount of parallax motion 410 of each of the layers can indicate movement or differences between the frames.
- the different layers 402 , 404 , and 406 can each have different amounts of parallax motion.
- an artificially rendered image is generated using this parallax effect.
- a set of control points (not shown) is moved perpendicular to a trajectory between a first frame and a second frame.
- the first frame includes a first image captured from a first location and the second frame includes a second image captured from a second location and each control point is moved based on an associated depth of the control point.
- control points located at a further depth are moved less than control points at a closer depth in some examples.
- each pixel in the first image corresponds to a control point in the set of control points.
- an artificially rendered image corresponding to a third location outside of the trajectory is generated by extrapolating individual control points using the set of control points for the third location and extrapolating pixel locations using the individual control points.
- extrapolating the pixel locations includes extrapolation using barycentric coordinates based on three or more control points.
- extrapolating the pixel locations includes extrapolation using splines.
- extrapolating the pixel locations includes extrapolation using finite elements.
- extrapolating the pixel locations includes using motion of a nearest control point.
- extrapolating pixel locations using the individual control points includes using dense optical flow.
- FIG. 7 shown is an example of a process for extrapolating a viewpoint outside the trajectory between two frames.
- This process can be viewed as an extension to the process described with regard to FIG. 3 , where viewpoints for locations outside of the trajectory between Frame A and Frame B are extrapolated by applying a transform which warps the image according the curvature and direction of the estimated path.
- a transform is applied to estimate a path outside the trajectory between a first frame and a second frame at 702 .
- the first frame includes a first image captured from a first location and the second frame includes a second image captured from a second location.
- the first image and second image are associated with the same layer.
- this process can be applied to various layers and the resulting images representing the layers can be combined to generate a complete image.
- an artificially rendered image corresponding to a third location positioned on the path is generated.
- the artificially rendered image is generated by first interpolating a transformation from the first location to the third location and from the third location to the second location at 704 .
- Image information is then gathered from the first frame and the second frame at 706 by transferring first image information from the first frame to the third frame based on the interpolated transformation and second image information from the second frame to the third frame based on the interpolated transformation. Finally, the first image information and the second image information is combined at 708 .
- the transform can implement various features.
- the transform can implement features such as homography, spherical projection, and/or cylindrical projection. These features can be used alone or in conjunction with one another in applying the transform. Additional features not mentioned can also be implemented within the scope of this disclosure.
- the present embodiment describes one method of extrapolating a viewpoint outside the trajectory between two frames.
- additional methods for extrapolating such viewpoints can also be implemented in other embodiments.
- another way to extrapolate viewpoints for locations outside of the trajectory between two frames can include moving the layers and/or control points perpendicular to the trajectory direction based on their depth. Specifically, layers/control points at greater depth are moved less than layers/control points at lesser depth.
- different methods can be chosen to provide an appropriate result.
- FIG. 8 shown is an example of a process for interpolating a layer using a panoramic representation.
- the span of a layer is not restricted to two frames but is constructed from multiple frames.
- a panorama is created from the corresponding background regions and combined into a panoramic representation.
- This panorama representation is then used to interpolate the corresponding layer.
- this process is described in terms of a background layer, this process can also be extended to other types of layers. For instance, the process can be applied to layers that include objects, such as context layers.
- a set of control points is tracked between multiple frames associated with the same layer at 802 .
- each pixel corresponds to a control point in the set of control points.
- each pixel is then interpolated based on the set of control points for the artificially rendered image.
- a panoramic representation is generated from the multiple frames at 804 .
- Individual control points are then interpolated from the set of control points at 806 for a desired frame or layer.
- Pixel locations are then interpolated for this frame or layer using the individual control points at 808 . From this interpolation, the artificially rendered image/layer is generated.
- interpolation can be implemented using a variety of methods.
- interpolating the pixel locations can include interpolation using barycentric coordinates based on three or more control points.
- interpolating the pixel locations includes interpolation using splines.
- interpolating the pixel locations includes interpolation using finite elements.
- interpolating the pixel locations includes using motion of a nearest control point.
- interpolating pixel locations using the individual control points includes using dense optical flow in some applications.
- a surround view is a multi-view interactive digital media representation.
- the surround view is generated from multiple images having location information, and at least a portion of overlapping subject matter.
- the surround view is generated by interpolating between the multiple images and/or extrapolating data from the multiple images.
- a surround view can be decomposed into one or more background panoramic representations and, in some examples, one or more foreground 3D models.
- An image at a specific viewpoint is then rendered by projecting the 3D model(s) and the panoramic representation(s) into a frame according to the current viewpoint and the depth and orientation of the 3D model(s).
- a content model is obtained from a surround view at 902 .
- This content model is a foreground three-dimensional model including at least one object.
- obtaining the content model from the surround view includes extracting the content model from the surround view.
- a context model is obtained from the surround view.
- This context model is a background panoramic representation including scenery surrounding any objects in the content model.
- obtaining the context model from the surround view includes extracting the context model from the surround view.
- the content model is projected into a frame corresponding to a desired viewpoint at 906 .
- the context model is also projected into the frame corresponding to the desired viewpoint at 908 .
- the content model and context model are projected according to the depth and orientation of the content model.
- the artificially rendered image at the desired viewpoint is generated from a combination of the projected content and context at at 910 .
- one or a combination of methods can be used to generate a single artificially rendered image.
- one layer can be handled using the method described with regard to FIG. 3
- another layer can be handled using the method described with regard to FIGS. 5A-5B
- a background layer can be handled using the method described with regard to FIG. 8 .
- Any combination of methods can be used within the scope of this disclosure.
- any one method can be used alone to generate an artificially rendered image.
- the methods presented in this disclosure are highly efficient and can be run on a mobile platform in real-time.
- the various methods require only very small amounts of additional data and are therefore extremely well-suited for data that needs to be sent over a wireless network (e.g. LTE).
- the methods are sufficiently regularized to handle problematic cases without creating jarring artifacts. Accordingly, the methods described herein are suitable for generating artificially rendered images on mobile devices, etc.
- a mobile device, computer system, etc. can be used to generate artificially rendered images.
- FIG. 10 shown is a particular example of a computer system that can be used to implement particular examples of the present invention.
- the computer system 1000 can be used to provide generate artificially rendered images according to various embodiments described above.
- the computer system 1000 shown can represent a computing system on a mobile device.
- a system 1000 suitable for implementing particular embodiments of the present invention includes a processor 1001 , a memory 1003 , an interface 1011 , and a bus 1015 (e.g., a PCI bus).
- the interface 1011 may include separate input and output interfaces, or may be a unified interface supporting both operations.
- the processor 1001 When acting under the control of appropriate software or firmware, the processor 1001 is responsible for such tasks such as optimization.
- Various specially configured devices can also be used in place of a processor 1001 or in addition to processor 1001 .
- the complete implementation can also be done in custom hardware.
- the interface 1011 is typically configured to send and receive data packets or data segments over a network.
- Particular examples of interfaces the device supports include Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like.
- various very high-speed interfaces may be provided such as fast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces and the like.
- these interfaces may include ports appropriate for communication with the appropriate media.
- they may also include an independent processor and, in some instances, volatile RAM.
- the independent processors may control such communications intensive tasks as packet switching, media control and management.
- the system 1000 uses memory 1003 to store data and program instructions and maintain a local side cache.
- the program instructions may control the operation of an operating system and/or one or more applications, for example.
- the memory or memories may also be configured to store received metadata and batch requested metadata.
- the present invention relates to tangible, machine readable media that include program instructions, state information, etc. for performing various operations described herein.
- machine-readable media include hard disks, floppy disks, magnetic tape, optical media such as CD-ROM disks and DVDs; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and programmable read-only memory devices (PROMs).
- program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.
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